European Journal of Advances in Engineering and Technology

1-6

Authors: Hamid Ali Abed Al-Asadi

A wireless sensor node is poised by a sensor, processor, confined memory, transceiver and a low-powered battery. To lessen the data communication period and energy intake, the sensor nodes are accumulated into a quantity of minute clutches denoted as clusters and the occurrence is mentioned as clustering. Essentially, clustering could be categorized into centralized, distributed and hybrid clustering methodologies. In centralized clustering method, the cluster head (CH) is immobile. The rest of the sensor nodes in the cluster deed as cluster member nodes. In case of distributed clustering mechanism, the CH is not static. The CH keeps on fluctuating form one sensor node to another sensor node within the cluster on the base of few fixed constraints. Hybrid clustering is the grouping of both centralized clustering and distributed clustering mechanisms. A dynamic clustering algorithm for mobile wireless sensor networks, the mobile clustering mechanism (MCM) has been examined and analysed appropriately. The anticipated method is hierarchical, dynamic and energy efficient algorithm. This system displays numerous clusters, with each clusters having a unique CH and two deputy CHs. The sensors start gathering the data only when the base station approaches in range with the cluster head. The performance of the projected algorithm has been assessed against the present LEACH-Mobile algorithm. This approach displays a large decrease in average communication energy and node death rate. The network lifetime has been extended by assimilating the fresh concepts to the proposed methodology, thereby finds valuable when both the nodes and the base station are moderately moveable.

21-30

Authors: Amieibibama Joseph, Joseph Atubokiki Ajienka and Oyeizugbe Samuel Osita

The productivity index (PI) is one useful tool used in assessing well productivity in the oil industry. As the reser-voir pressure declines during production, the decline in well productivity becomes inevitable following the deple-tion of the hydrocarbon in place. Hence, the well productivity index declines over time even as the strength of the predominant drive mechanism deteriorates and becomes less significant in hydrocarbon recovery. Production de-cline can also result from impairment of the formation and poor completion designs. Therefore, PI decline of wells draining a reservoir suggests reserve depletion while exceptions suggest formation damage for low PI wells. Con-sequently, the PI values should be compared to the well completion efficiency to know if the decline is normal or induced by formation damage. This study presents procedure for analyzing PI decline curves and its application to well surveillance. Different representative rate of decline curves and their respective critical times were developed for determining stimulation candidate for different sand exclusion types. The possible areas of application include ranking candidates for stimulation as well as predicting the critical time for stimulating wells in order to improve production in oil wells.

36-43

Authors: Musa Mustapha, BU Musa, GA Bakare, AL Bukar, Babangida Modu,ZA Gwoma, UA Benisheikh and AB Buji

Unified Power Flow Controller (UPFC) is a versatile FACTS device which can provide full dynamic control of a transmission line parameters, bus voltage, line impedance and phase angle for improved system security. However, the extent that performance of UPFC can be brought out, it greatly dependent upon the location and size of this device in the system. This paper present Differential Evolution (DE) Technique for optimal placement of UPFC device under steady state (i.e normal, moderate and critical) conditions and is aimed at real power loss minimization and voltage profile enhancement of an interconnected power system. The proposed method is applied on the IEEE 14 bus power system which is demonstrated through MATPOWER simulation package. The results obtained indicate that DE is robust and can significantly enhance the security of power system by reducing line losses and improved bus voltage profile within the acceptable limit.

49-55

Authors: Oloyede Ayodele and Aderonke Adegbenjo

For the entire period of recorded time, floods have been a major cause of loss of life and property. Methods of pre-diction and mitigation range from human observers to sophisticated surveys and statistical analysis of climatic data. In the last few years, researchers have applied computer programs called Neural Networks or Artificial Neural Networks and fuzzy logic to a variety of uses ranging from medical to financial. The purpose of this study is to demonstrate that fuzzy logic based model can be successfully applied to flood forecasting. This study explores the potential of the fuzzy and neuro fuzzy model process for forecasting flood. In this study, a neurofuzzy approach is proposed to forecast flood from hydrologic data of rainfall, temperature, water level, sediment and discharge. The ten years’ hydrologic data recorded on daily basis that were collected from oyan and owena gauging station were trained and used for the models development and simulation.

64-70

Authors: Adnan Majeed, Sajid Mehmood, Kaleem Sarwar, Ghulam Nabi, M Ashiq Kharal

Several methods are available for estimatingreference evapotranspiration (ETo) which requiremany weathervariablesthatare notalways availableatallweatherstations specially in developing countries.TheHargreaves equation(HG)requiresonly daily air temperature data and extraterrestrial radiation for ETo estimates. The HG method oftentendstooverestimateorunderestimateETo, so it become necessary before using HG method it must be calibrated according to local conditions.The HG equationwasevaluated undersemiarid conditionsby using15, 10 and 9-years ofcomplete daily climaticdatafromtheBahawalpur, Bahawalnagar and Khanpurweatherstations of Southern Punjab, Pakistan, respectively.TheHG methodwascomparedtoFAO56PenmanMonteithequation(PM).TheoriginalHG equation overestimated for all time steps of three weather stations. TheoriginalHG equation overestimated ETo by14,12,and8 % fordaily,10-daily (decade),and monthly ETo for Bahawalpur and by 8, 6 and 5 % of Bahawalnagar and by 25, 10 and 8% of Khanpur stations, respectively, as compared to PM equation. A simple mathematical logic applied to obtain the modified HG coefficients for all time steps showed that the modified HG equation improved the results of ETo estimation to 4, 3, and 2% for daily, 10-daily and monthly time steps of Bahawalpur station 4, 2.5 and 2% of Ba-hawalnagar station and 6, 3.5 and 3% of Khanpur station, difference from ETo computed by the PM method, with root mean square error (RMSE) of 1.411, 1.105 and 0.950 mm d-1 for daily, 10-daily, and monthly ET of Bahawal-pur and 1.800, 1.121 and 0.865 mm d-1 of Bahawalnagar and 1.13, 0.971 and 0.595 mm d-1 of Khanpur stations, respectively. For more accuracy, further improvement in modified HG method achieved by adding the wind speed, reaching an average difference of 1% for all timescales. The original HG equation overestimated for all time steps before modification.